14–17 Oct 2019

Schedule: Deep Learning sessions

Add to your personal schedule
9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Park Suite
Michael Cullan (Pragmatic Institute)
Average rating: ****.
(4.00, 1 rating)
The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. Michael Cullan walks you through TensorFlow's capabilities in Python, from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
Add to your personal schedule
9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Westminster Suite
Rich Ott (The Pragmatic Institute)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Rich Ott to get the knowledge you need to build deep learning models using real-world datasets and PyTorch. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
Pramod Singh (Walmart Labs ), Akshay Kulkarni (Publicis Sapient)
Average rating: **...
(2.40, 10 ratings)
An estimated 80% of data generated is an unstructured format, such as text, an image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0. Read more.
Add to your personal schedule
11:0511:45 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Qun Ying (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and explain how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
Add to your personal schedule
11:0511:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Alex Ingerman (Google)
Average rating: ****.
(4.29, 7 ratings)
Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated. Read more.
Add to your personal schedule
11:5512:35 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Biraja Ghoshal (Tata Consultancy Service)
Average rating: *....
(1.00, 2 ratings)
Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis, but knowing how much confidence there is in a prediction is essential for gaining clinicians' trust. Biraja Ghoshal explores probabilistic modeling with TensorFlow Probability in cancer prediction. Read more.
Add to your personal schedule
11:5512:35 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Average rating: ****.
(4.00, 5 ratings)
Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been used for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting. Read more.
Add to your personal schedule
13:4514:25 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Douglas Calegari (Independent)
Average rating: ****.
(4.00, 3 ratings)
Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system. Read more.
Add to your personal schedule
14:3515:15 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Abhishek Kumar (Publicis Sapient)
Abhishek Kumar outlines how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow. Read more.
Add to your personal schedule
16:0016:40 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Rajib Biswas (Ericsson)
Average rating: ****.
(4.00, 2 ratings)
Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions. Read more.
Add to your personal schedule
16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Average rating: ****.
(4.25, 12 ratings)
Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure. Read more.
Add to your personal schedule
11:0511:45 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Michael Mahoney (UC Berkeley)
Average rating: ***..
(3.00, 4 ratings)
Developing theoretically principled tools to guide the use of production-scale neural networks is an important practical challenge. Michael Mahoney explores recent work from scientific computing and statistical mechanics to develop such tools, covering basic ideas and their use for analyzing production-scale neural networks in computer vision, natural language processing, and related tasks. Read more.
Add to your personal schedule
11:5512:35 Thursday, 17 October 2019
Location: Westminster Suite
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Average rating: ****.
(4.80, 5 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices. Read more.
Add to your personal schedule
11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
Average rating: *****
(5.00, 1 rating)
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data. Read more.
Add to your personal schedule
14:3515:15 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Carlos Rodrigues (Siemens)
Average rating: *****
(5.00, 3 ratings)
An evolving landscape of cyber threats demands innovation. It's time to bring AI to the fight. Carlos Rodrigues explains why it's mandatory to use bleeding-edge AI in production to improve threat detection in a worldwide company such as Siemens. The corporate network has more than 500,000 endpoint and more than 370,000 employees. The attack vectors are endless; thus, legacy approaches don't scale. Read more.

Contact us

confreg@oreilly.com

For conference registration information and customer service

partners@oreilly.com

For more information on community discounts and trade opportunities with O’Reilly conferences

aisponsorships@oreilly.com

For information on exhibiting or sponsoring a conference

pr@oreilly.com

For media/analyst press inquires